This project features an implementation of the Parallel STL library using the Khronos SYCL standard.
Parallel STL is an implementation of the Technical Specification for C++ Extensions for Parallelism, current document number N4507. This technical specification describes a set of requirements for implementations of an interface that computer programs written in the C++ programming language may use to invoke algorithms with parallel execution. In practice, this specification is aimed at the next C++ standard (C++20) and offers the opportunity to users to specify execution policies to traditional STL algorithms which will enable the execution of those algorithms in parallel. The various policies can specify different kinds of parallel execution. For example,
std::vector<int> v = ...
// Traditional sequential sort
std::sort(vec.begin(), vec.end());
// Explicit sequential sort
std::sort(seq, vec.begin(), vec.end());
// Explicit parallel sort
std::sort(par, vec.begin(), vec.end());
SYCL is a royalty-free, cross-platform C++ abstraction layer that builds on top of OpenCL. SYCL enables single-source development of OpenCL applications in C++ whilst enabling traditional host compilers to produce standard C++ code.
SyclParallelSTL exposes a SYCL policy in the experimental::parallel namespace that can be passed to standard STL algorithms for them to run on SYCL. Currently, only some STL algorithms are implemented, such as:
- sort : Bitonic sort for ranges where the size is a power of two, or sequential sort otherwise.
- transform : Parallel iteration (one thread per element) on the device.
- fill : Parallel iteration (one thread per element) on the device.
- fill_n : Parallel iteration (one thread per element) on the device.
- generate : Parallel iteration (one thread per element) on the device.
- generate_n : Parallel iteration (one thread per element) on the device.
- for_each : Parallel iteration (one thread per element) on the device.
- for_each_n : Parallel iteration (one work-item per element) on the device.
- replace : Parallel iteration (one thread per element) on the device.
- replace_if : Parallel iteration (one thread per element) on the device.
- replace_copy : Parallel iteration (one thread per element) on the device.
- replace_copy_if : Parallel iteration (one thread per element) on the device.
- reverse: Parallel iteration (one work-item per 2 elements) on device.
- reverse_copy : Parallel iteration (one work-item per element) on the device.
- count : Parallel iteration (one work-item per 2 elements) on device.
- count_if : Parallel iteration (one work-item per 2 elements) on device.
- reduce : Parallel iteration (one work-item per 2 elements) on device.
- inner_product: Parallel iteration (one work-item per 2 elements) on device.
- transform_reduce : Parallel iteration (one work-item per 2 elements) on device.
- inclusive_scan : Parallel iteration (one work-item per 2 elements) on device.
- exclusive_scan : Parallel iteration (one work-item per 2 elements) on device.
- mismatch : Parallel iteration (one work-item per 2 elements) on device.
- all_of: Parallel iteration (one work-item per 2 elements) on device.
- any_of: Parallel iteration (one work-item per 2 elements) on device.
- none_of: Parallel iteration (one work-item per 2 elements) on device.
- equal: Parallel iteration (one work-item per 2 elements) on device.
Some optimizations are implemented. For example:
- the ability to pass iterators to buffers rather than STL containers to reduce the amount of information copied in and out
- the ability to specify a queue to the SYCL policy so that the queue is used for the various kernels (potentially enabling asynchronous execution of the calls).
This project currently supports the SYCL beta implementation from Codeplay, ComputeCPP and the open-source triSYCL implementation.
The project uses CMake 3.5 in order to produce build files, but more recent versions may work.
In Linux, simply create a build directory and run CMake as follows:
$ mkdir build
$ cd build
$ cmake ../ -DCOMPUTECPP_PACKAGE_ROOT_DIR=/path/to/sycl \
$ make
Usual CMake options are available (e.g. building debug or release). Makefile and Ninja generators are supported on Linux.
To simplify configuration, the FindComputeCpp cmake module from the ComputeCPP
SDK is included verbatim in this package within the cmake/Modules/ directory.
If Google Mock is found in external/gmock, a set of unit tests is built. Unit tests can be run by running Ctest in the binary directory. To install gmock, run the following commands from the root directory of the SYCL parallel stl project:
$ mkdir external
$ cd external
$ git clone git@github.com:google/googletest.git
$ cd googletest/googlemock/make
$ make
To enable building the benchmarks, enable the PARALLEL_STL_BENCHMARKS option
in the cmake configuration line, i.e. -DPARALLEL_STL_BENCHMARKS=ON.
When building with a SYCL implementation that has no device compiler, enable the SYCL_NO_DEVICE_COMPILER option to disable the specific CMake rules for intermediate file generation.
Refer to your SYCL implementation documentation for implementation-specific building options.
To quickly build the project and run some non-regression tests with
benchmarks, you can use the script build.sh:
If you want to compile it with ComputeCpp:
./build.sh "path/to/ComputeCpp" (this path can be relative)
for example (on Ubuntu 16.04):
./build.sh ~/ComputeCpp
If you want to compile it with triSYCL:
./build.sh --trisycl [-DTRISYCL_INCLUDE_DIR=path/to/triSYCL/include] [-DBOOST_COMPUTE_INCLUDE_DIR=path/to/boost/compute/include] [-DTRISYCL_OPENCL=ON]
for example (on Ubuntu 16.04):
./build.sh --trisycl -DTRISYCL_INCLUDE_DIR=~/triSYCL/include -DBOOST_COMPUTE_INCLUDE_DIR=~/compute/include [-DTRISYCL_OPENCL=ON]
or if Boost compute is in your library's default path, just with:
./build.sh --trisycl -DTRISYCL_INCLUDE_DIR=~/triSYCL/include [-DTRISYCL_OPENCL=ON]
Just run build.sh alone to get a small help message.
For triSYCL some benchmarks may display messages saying that unimplemented features are used, you can ignore those messages as these features do not affect the benchmarks executions, if you wish you can also contribute to the triSYCL implementation to make those messages definitely disapear.
Source code is documented using Doxygen. To build the documentation as an HTML file, navigate to the doc directory and run doxygen from there.
$ cd doc
$ doxygen
This will generate the html pages inside the doc_output directory.
-
The Lambda functions that you can pass to the algorithms have the same restrictions as any SYCL kernel. See the SYCL specification for details on the limitations.
-
While using lambda functions, the compiler needs to find a name for that lambda function. To provide a lambda name, the user has to do the following:
cl::sycl::queue q; sycl::sycl_execution_policy snp(q); sort(snp, v.begin(), v.end(), [=](int a, int b) { return a >= b; });
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Be aware that some algorithms may run sequential versions if the number of elements to be computed are not power of two. The following algorithms have this limitation: sort, inner_product, reduce, count_if and transform_reduce.
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Refer to SYCL implementation documentation for implementation-specific building options.
Intel and the Intel logo are trademarks of Intel Inc. AMD, the AMD Arrow logo, and combinations thereof are trademarks of Advanced Micro Devices, Inc. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos. Other names are for informational purposes only and may be trademarks of their respective owners.